Improving the Effect of Electric Vehicle Charging on Imbalance Index in the ‎Unbalanced Distribution Network Using Demand Response Considering Data ‎Mining Techniques

Document Type : Research paper


1 Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

2 Northumbria University, Electrical Power and Control Systems Research Group, Ellison Place NE1 8ST, Newcastle ‎upon Tyne, United Kingdom


With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.


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